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Strip surface defect recognition method based on multi-manifold learning

A defect recognition and multi-manifold technology, applied in character and pattern recognition, instruments, computer components, etc., can solve problems such as poor classification effect and insignificant application effect

Inactive Publication Date: 2016-09-14
WUHAN UNIV OF SCI & TECH
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  • Description
  • Claims
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Problems solved by technology

However, when classifying, traditional data analysis methods such as neural network, wavelet analysis, nuclear local preservation projection, independent component analysis and principal component analysis are still used, and the classification effect is not good, and the application effect is not significant.

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  • Strip surface defect recognition method based on multi-manifold learning
  • Strip surface defect recognition method based on multi-manifold learning
  • Strip surface defect recognition method based on multi-manifold learning

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Embodiment 1

[0050] A strip steel surface defect recognition method based on multi-manifold learning, the specific steps are:

[0051] (1) Perform grayscale processing, smoothing processing, normalization processing and vectorization on the original collected strip surface defect image in sequence to obtain a pair of vector data points X after preprocessing of the strip surface defect image i , the vector data X of all strip surface defect images preprocessed i Form matrix data X. In this embodiment, 4 types of strip steel surface defect images are collected, and the defect categories of the 4 types of strip steel surface defect images refer to weld seams, edge serrations, inclusions and head lines; each type of data has a total of 40 pieces, and each piece has a size of 100* 100, a pair of vector data points X after preprocessing of the strip surface defect image i is 10000 dimensions, all strip surface defect image preprocessed vector data X i Form the matrix data X of 160*10000.

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Abstract

The invention relates to a strip steel surface defect identification method based on multiple manifold learning. According to the technical scheme, for the vector data point Xi of vectorized surface defect image of any strip steel, K neighbor points of the same category and different categories are respectively selected to build up corresponding similar data sub graph and heterogeneous data sub graph; the minimum error linear representation coefficient matrix Wintra of the similar data sub graph and the minimum error linear representation coefficient matrix Winter of the heterogeneous data sub graph are calculated; similar data sub graph divergence Sinter and heterogeneous data sub graph divergence Sintra are respectively built up; the difference between the heterogeneous data sub graph divergence Sinter and the similar data sub graph divergence Sintra is maximized to find a low dimensional projection matrix A; and after low dimensional projection, the category of the strip steel surface defect image whose category is unknown is judged by using a nearest neighbor method. According to the invention, through local linear representation, the local structure of each manifold is detected, and the identification rate of the strip steel surface defect image can be improved.

Description

technical field [0001] The invention belongs to the technical field of strip steel surface defect identification. In particular, it relates to a strip surface defect recognition method based on multi-manifold learning. Background technique [0002] Strip steel is one of the main product forms of the steel industry, and is an essential raw material for aerospace, automobile and ship manufacturing, etc. Therefore, the quality inspection of strip steel is particularly important, which is related to the development of many manufacturing industries, and the surface quality is one of the most important quality factors of strip steel, and is an important condition for enterprises to win the market. Therefore, the detection of strip surface quality is receiving more and more attention due to its important practical value. [0003] Steel plate surface quality inspection has gone through three stages of development: manual visual inspection, traditional non-destructive inspection an...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/46
Inventor 李波田贝贝张晓龙
Owner WUHAN UNIV OF SCI & TECH
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